Notebook
切换cuda版本以及cudnn版本
ln -s 源 链接
ls -al 可以看到链接的目的地
装cuda10.0:在centos上装不要用rpm文件装，会报依赖问题，要用.run装
装cuda不同版本，一般是创建一个软连接到/usr/local/cuda；然后在~/.bashrc中添加
export PATH=$PATH:/usr/local/cuda/bin export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
如果报错
missing CUDA_INCLUDE_DIRS：
则在~/.bashrc中添加
export CUDA_INCLUDE_DIRS=/usr/local/cuda/extras/CUPTI/include
以后切换不同的cuda就官网下载对应的文件，然后装好之后，将以前的软连接删除，然后链接到新的cuda文件夹就好，然后nvcc -V查看cuda版本
ubuntu升级显卡驱动
sudo add-apt-repository ppa:graphics-drivers/ppa && sudo apt update
在图形界面这么装比较放心
装了个430开不了机了，卸载重装
[cuda与driver对应表格]https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
https://blog.csdn.net/EliminatedAcmer/article/details/80528980
ubuntu装显卡驱动
装完cuda9.2，开机闪烁光标，
不能偷懒用
这里的装，装完必有问题
ffmpeg 上下拼接视频
ffmpeg -i 2-0001.mkv -i 2-0001.mkv -filter_complex vstack=inputs=2 -t 2 output.mp4
2019年10月12日
编译的时候，如果某些库链不上
ldd
locate
没有的话装库：https://pkgs.org/
可以在此网站搜索
export LD_LIBRARY_PATH=/data/zhuyinghao/vino_inference_sdk/vino/cv/py3/sys:$LD_LIBRARY_PATH
https://answers.opencv.org/question/145214/convert-cvmat-to-stdvector-without-copying/
time ./ffmpeg -i ${input} -vf qrestore2=model=torch_yuv:model_path=./qrestore_model/yuv_train_net_70.pt:device=cpu:denoise=0 -loglevel info -qscale 0 -y ./${name}_torch_yuv.mp4 &>> cal_time.log
time ./ffmpeg -i ${input} -vf qrestore2=model=torch_rgb:model_path=./qrestore_model/model_0422_190_G.pt:device=cpu:denoise=0 -loglevel info -qscale 0 -y ./${name}_torch_rgb.mp4 &>> cal_time.log
time ./ffmpeg -i ${input} -vf qrestore2=model=vino_rgb:model_path=/data/zhuyinghao/qrestore_inference/code/qrestore2.0_cpp_use_libtorch/qrestore_vino/model/model_190_net_G.xml:vino_extension=/opt/vino_inference_sdk/vino/lib64/libcpu_extension.so:device=cpu:denoise=0 -loglevel info -qscale 0 -y ./${name}_torch_vino_rgb.mp4 &>> cal_time.loglibav编译时出错：
libavcodec/libx264.c: In function ‘X264_frame’:
libavcodec/libx264.c:246:9: error: ‘x264_bit_depth’ undeclared (first use in this function)
if (x264_bit_depth > 8)
^
libavcodec/libx264.c:246:9: note: each undeclared identifier is reported only once for each function it appears in
libavcodec/libx264.c: In function ‘X264_init_static’:
libavcodec/libx264.c:707:9: error: ‘x264_bit_depth’ undeclared (first use in this function)
if (x264_bit_depth == 8)
看了一圈没几个正经解决方案，参考x264对ffmpeg的补丁解决 http://git.videolan.org/?p=ffmpeg.git;a=patch;h=2a111c99a60fdf4fe5eea2b073901630190c6c93
scl enable devtoolset-3 bash
-DCMAKE_BUILD_TYPE=Release -DCUDA_nppi_LIBRARY=true
cmake -D WITH_CUDA=ON WITH_CUBLAS=ON WITH_CUFFT=ON WITH_NVCUVIDs=ON CUDA_FAST_MATH=ON CUDA_GENERATION=Auto -D OPENCV_EXTRA_MODULES_PATH=../opencv_contrib/modules ..
-- Registering hook 'INIT_MODULE_SOURCES_opencv_dnn': /data/opencv_gpu/opencv-master/modules/dnn/cmake/hooks/INIT_MODULE_SOURCES_opencv_dnn.cmake CMake Error at modules/dnn/CMakeLists.txt:97 (message): CUDA backend for DNN module requires CC 5.3 or higher. Please remove unsupported architectures from CUDA_ARCH_BIN option.
解决：
cmake文件中查找__cuda_arch_bin
将５．３以下的去掉
if(CUDA_VERSION VERSION_LESS "9.0")
#set(__cuda_arch_bin "3.0 3.5 3.7 5.0 5.2 6.0 6.1")
set(__cuda_arch_bin "6.0 6.1")
#set(CUDA_ARCH_BIN "3.0 3.5 3.7 5.0 5.2 6.0 6.1")
elseif(CUDA_VERSION VERSION_LESS "10.0")
set(__cuda_arch_bin "6.0 6.1 7.0")
#set(CUDA_ARCH_BIN "3.0 3.5 3.7 5.0 5.2 6.0 6.1 7.0")
else()
set(__cuda_arch_bin "6.0 6.1 7.0 7.5")
#set(CUDA_ARCH_BIN "3.0 3.5 3.7 5.0 5.2 6.0 6.1 7.0 7.5")
endif()cmake成功，但编译报错
后来cmake时指定CUDA_ARCH_BIN
cmake -D WITH_CUDA=ON WITH_CUBLAS=ON WITH_CUFFT=ON WITH_NVCUVIDs=ON CUDA_FAST_MATH=ON -D CUDA_ARCH_BIN=6.1 CUDA_GENERATION=Auto -D OPENCV_EXTRA_MODULES_PATH=../opencv_contrib/modules ..
cmake -D WITH_CUDA=ON WITH_CUBLAS=ON WITH_CUFFT=ON WITH_NVCUVIDs=ON CUDA_FAST_MATH=ON -D CUDA_ARCH_BIN=6.1 CUDA_GENERATION=Auto -DCUDA_nppi_LIBRARY=true ..
Please make sure that
- PATH includes /data/cuda-10.0/bin
- LD_LIBRARY_PATH includes /data/cuda-10.0/lib64, or, add /data/cuda-10.0/lib64 to /etc/ld.so.conf and run ldconfig as root
安装cmake
wget https://cmake.org/files/v3.6/cmake-3.6.2.tar.gz
tar xvf cmake-3.6.2.tar.gz && cd cmake-3.6.2/
./bootstrap
gmake -j8
gmake install
/usr/local/bin/cmake --version
删除原来cmake版本，建立软连接，测试
yum remove cmake -y
ln -s /usr/local/bin/cmake /usr/bin/
cmake --version
编译出现这个问题：
centos cmake ./cmVersionConfig.h:7:1: error: missing terminating " character
发现时下载的包出现了问题，一开始我是下的zip，不管什么版本都会出现这个问题，换成tar.gz问题结解决
select input_param,sharpness from Framework_quality_check where sharpness>=0.9 and sharpness<=1 and start_time between '2019-09-05' and '2019-10-31' and input_param like '%zongyi%' and input_param like '%mkv%' and input_param like '%fileLocation%'
basename example.tar.a.b.c.gz .c.gz
# =&gt; example.tar.a.b
FILE=&quot;example.tar.gz&quot;
echo &quot;${FILE%%.*}&quot; 取头 example
# =&gt; example
echo &quot;${FILE%.*}&quot; 去尾 example.tar.a.b.c
# =&gt; example.tar
echo &quot;${FILE#*.}&quot; 去头 tar.a.b.c.gz
# =&gt; tar.gz
echo &quot;${FILE##*.}&quot; 取尾 gz
# =&gt; gz
# 在bash中可以这么写
filename=$(basename &quot;$fullfile&quot;)
extension=&quot;${filename##*.}&quot;
filename=&quot;${filename%.*}&quot;
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版权声明：本文为CSDN博主「RonnyJiang」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/RonnyJiang/article/details/52386121real 0m0.109s
user 0m0.080s
sys 0m0.029s
bjwyx-6min.mkv
real 91m58.011s
user 269m10.084s
sys 18m30.749s
bjwyx-6min_restore.mp4
real 87m1.553s
user 287m2.318s
sys 17m56.803s
jd-28-6min.mkv
real 58m43.122s
user 149m8.490s
sys 11m24.640s
jd-28-6min_restore.mp4
real 43m23.533s
user 111m13.592s
sys 8m45.605s
jqdsy-6min.mkv
没有denoise的gpu运行时间
6min-restore real 0m12.370s user 0m0.089s sys 0m0.049s bjwyx-6min.mkv real 84m51.770s user 309m48.195s sys 18m10.691s bjwyx-6min_restore.mp4 real 79m12.773s user 325m5.459s sys 17m58.529s bjwyx-6min_restore_restore.mp4 real 82m40.649s user 340m15.740s sys 18m36.421s jd-28-6min.mkv real 51m20.795s user 174m21.728s sys 9m16.073s jd-28-6min_restore.mp4 real 51m9.927s user 180m22.828s sys 9m10.829s jd-28-6min_restore_restore.mp4 real 37m47.379s user 133m48.595s sys 6m44.176s jqdsy-6min.mkv real 51m31.423s user 212m46.812s sys 9m48.838s jqdsy-6min_restore.mp4 real 0m0.161s user 0m0.079s sys 0m0.031s nezha_6min.mkv real 50m18.672s user 175m14.706s sys 8m56.598s swsqy-6min.mkv real 50m29.394s user 165m20.706s sys 8m30.871s wjfy-6min.mkv real 100m38.379s user 364m8.060s sys 18m27.765s yhbxb-6min.mkv real 48m45.559s user 166m35.977s sys 8m44.169s
select input_param,sharpness from Framework_quality_check where sharpness &gt;0 and sharpness &lt;1 and start_time between &#39;2019-09-05&#39; and &#39;2019-11-12&#39; and input_param like &#39;%zongyi%&#39; and input_param like &#39;%mkv%&#39;
对ffmpeg gpu valgrind
==334586== ==334586== HEAP SUMMARY: ==334586== in use at exit: 2,101,652,496 bytes in 1,608,370 blocks ==334586== total heap usage: 3,532,073 allocs, 1,923,703 frees, 4,400,394,734 bytes allocated ==334586== ==334586== LEAK SUMMARY: ==334586== definitely lost: 4,024 bytes in 35 blocks ==334586== indirectly lost: 2,788 bytes in 33 blocks ==334586== possibly lost: 6,427,112 bytes in 47,545 blocks ==334586== still reachable: 2,095,218,572 bytes in 1,560,757 blocks ==334586== of which reachable via heuristic: ==334586== stdstring : 404,523 bytes in 5,807 blocks ==334586== newarray : 1,552 bytes in 17 blocks ==334586== suppressed: 0 bytes in 0 blocks ==334586== Rerun with --leak-check=full to see details of leaked memory ==334586== ==334586== For lists of detected and suppressed errors, rerun with: -s ==334586== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0)
yum install -y yum-utils centos-release-scl yum-config-manager --enable rhel-server-rhscl-7-rpms yum install -y devtoolset-7-gcc devtoolset-7-gcc-c++ devtoolset-7-gcc-gfortran devtoolset-7-binutils
说找不到包，改成
输出time命令
之前想输出time用如下命令无效：
time ls &amp;&gt; a.txt
后来发现以下有效
{ time ls; } &amp;&gt; a.txt注意大括号的空格，与;号
sftp -o ServerAliveInterval=30 -P 7122 root,10.16.195.37,22,zhuyinghao@jumpbox.qiyi.domain
强制覆盖本地代码
git fetch --all git reset --hard origin/master git pull
ppc竞品ftp
lftp cloud_codec:test_codec@10.110.27.67/2019PPC
将一张图左右裁剪成两个部分
ls -1 *.png | sed &#39;s,.*,&amp; &amp;,&#39; | xargs -n 2 convert -crop 50%x100% +repage
先bilateral,后restore
./ffmpeg -i /data/zhuyinghao/qrestore-2.0.1-test-dataset/zaosheng_dianying_8_2_noise.mkv -vf &quot;[in]bilateral=Diameter=9:sigmaColor=10:sigmaSpace=10[middle];[middle]qrestore2=model=torch_rgb:model_path=./qrestore_model/model_0422_190_G.pt:device=gpu:denoise=0[out]&quot; -loglevel info -c:v libx264 -x264opts qp=12:min-keyint=25:keyint=128 zaosheng_dianying_8_2_bila_restore.mp4
先hqdn3d，后restore
./ffmpeg -i dianshiju_2_1_argus_0.33.mkv -vf &quot;[in]hqdn3d=0:0:6:0[middle];[middle]qrestore2=model=torch_rgb:model_path=./qrestore_model/model_0422_190_G.pt:device=gpu:denoise=1:flat-weight=0.5:edge-weight=1[out]&quot; -loglevel info -c:v libx264 -x264opts qp=12:min-keyint=25:keyint=128 dianshiju_2_1_argus_0.33_hqdn_denoise.mp4
Glad to hear you found something that works for you!
Since you got me down the rabbit hole of denoisers, I figured I would share my research results with anyone who’s interested.
ffmpeg has six denoisers built-in that I was able to find, which I’ve listed below along with their transcoding speeds on a 1080p source video using a four-core laptop computer. I wrote scripts that used a variety of settings with each denoiser to make sure I was seeing the best each one had to offer.
atadenoise (20 fps) - by averaging pixels across frames, it reduces contrast of noise areas to make them less obvious as opposed to using a specialized algorithm to smooth the noise away; this reduces overall image contrast; filter also darkens the overall output
dctdnoiz (1.6 fps) - creates beautiful detail on a still image, but randomizes the noise across frames so much that it actually makes the noise look worse during playback, plus it darkens the output
nlmeans (0.6 fps) - darkens the output, but sometimes has redeeming qualities (more on this later)
hqdn3d (21 fps) - color neutral which is good, but the output looks smeary to me where it loses a lot of fine detail in hair strands and wood grain
owdenoise (0.3 fps) - color neutral wavelet denoiser with stunningly good results on high-res sources
vaguedenoiser (7.6 fps) - another color neutral wavelet denoiser whose output looks identical to owdenoise, but its processing speed is 25x faster; tried every combination of threshold and nsteps, and found the default settings of 2/6 to consistently produce the closest-to-real-life results
I tested the denoisers on videos I took with my own mirrorless camera, meaning I remember what the scene looked like in real life. In one video, there happened to be a guy in a black business dress shirt made of silk or satin or something with a sheen to it, but the sheen wasn’t coming through due to the noise of the original footage. The wavelet-based denoisers were the only ones to remove and smooth the noise such that the fabric regained the smooth sheen you would expect from silk. To my eye, it bumped up the realism of the video an entire notch to see fabric actually look like fabric. The rest of the frame also dropped to zero dancing noise. It turned the video into a still photograph when nothing was moving. I didn’t realize until this experiment that even a tiny amount of dancing noise can seriously detract from the realism of a video, and that a sense of immersion can be restored by getting rid of it. Obviously, vaguedenoiser is my new weapon of choice.
So, about nlmeans… I found a radical difference between the ffmpeg version and the HandBrake version. I think HandBrake wins on every metric. nlmeans in ffmpeg actually makes video look worse (blockier) if the resolution is 1080p or above, or if the video comes from an excellent camera that has little noise to begin with. nlmeans in ffmpeg also can’t be used as a finishing step because it darkens the output, which destroys any color grading that happened before it. But I found two places where nlmeans in ffmpeg outshined the other ffmpeg denoisers: low-resolution video, and very-high-noise video. nlmeans does great at restoring a VHS capture, which I sense from the author’s web site was one of the original design goals. Secondly, in my tests, nlmeans did better than the other ffmpeg denoisers on high-resolution high-noise videos, which in my case meant a smartphone video in low light using digital zoom. Given these two specialized cases where nlmeans performed well, I could see a workflow where I used nlmeans to create denoised intermediates, then color graded the intermediates to fix the darkened output. Running nlmeans on a noisy source then adding it to the timeline and running vaguedenoiser on the total project did not cause any harm in my tests. But for best results, I think HandBrake is still the way to go where nlmeans is involved.
For my purposes, I think I will stick to vaguedenoiser because it’s beautiful on 1080p and 4K, and it is easily added to my existing ffmpeg filter chain when I do my finishing steps. I don’t have to create an intermediate to pass off to HandBrake this way. However, if I came across a particularly noisy source video, I would probably run it through HandBrake before adding it to my Shotcut project to get the same benefits Andrew noticed.
Good luck to everyone, whatever you use.
ffmpeg 报错：Too many packets buffered for output stream 0:1.
-max_muxing_queue_size 1024
http://www.jasonbowdach.com/blog/2015/12/5-tips-on-noise-reduction.html
./ffmpeg -i fengrenji_xiaopangzi.mp4 -vf qrestore2=model=torch_rgb:model_path=./qrestore_model/model_0422_190_G.pt:roi=1:roi_model_path=./qrestore_model/run-0-final_cpu.pt:device=gpu -loglevel info -c:v libx264 -x264opts qp=12:min-keyint=25:keyint=128 fengrenji_roi_bila.mp4
Masking the foreground and denoising the background is a good technique when the background is out of focus.
You would get a slightly better result using a dedicated noise removal plugin (noiseninja or neatimage, or one of the inbuilt Photoshop tools). Gaussian blur is a bit of a blunt instrument, whilst it's good at removing pure random noise it is not as good at removing banding, which is visible in this image. It's also bad at preserving hard edges. An edge aware noise filter would let you mask much closer to the edges of your in-focus subject without risk of blurring it, which in turn would avoid the halo of grain you have around the bottle.
Blending a little (25% or 33%) of the original noisy background back in helps the result look less fake, whilst still allowing a low overall noise level.
Finally when you have a noisy image, save using the highest quality option if using JPEG (or use PNG). JPEG's attempt to compress noise often looks worse than the noise itself!
换显卡型号，要重新编译libtorch：
(1)下载源代码，注意要加上--recursive，否则下载的代码不全，否则编译时报错
git clone --recursive https://github.com/pytorch/pytorch -b v1.3.0
cd pytorch
if you are updating an existing checkout
git submodule sync
git submodule update --init --recursive
(2)编译时可以限制编译线程数，默认是80，在cpu核较少时，可能会崩溃；
export MAX_JOBS=0;
编译完libtorch，再编译ffmoeg出现以下错误：
是因为编译libtorch是用的python, 用的/root/anaconda3/lib/libstdc++.so.6.0.26 , 但是因为openvino 的配置脚本把libstdc++.so连接到了openvino中，
将/usr/lib64/libstdc++.so.6链接到 /root/anaconda3/lib/libstdc++.so.6.0.26就好了
编译过的ffmpeg运行时报
也是因为这个；
将/opt/vino_inference_sdk/tools/setupvar.sh中的这两行注释掉
可见性：export CUDA_VISIBLE_DEVICES=&quot;&quot;
CMakelist编写
cuda项目的CMakelist.txt
# 按惯例，cmake的版本
CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
# 项目名称
PROJECT(AD-Census)
# cmake寻找cuda，这个要现在系统里面装好cuda，设置好cuda的环境参数啥的
FIND_PACKAGE(CUDA REQUIRED)
# C++和CUDA的编译参数，可选。
SET(CMAKE_CXX_FLAGS &quot;${CMAKE_CXX_FLAGS} -std=c++11&quot;)
SET(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode arch=compute_61,code=sm_61;-std=c++11;)
# 头文件路径，按需
INCLUDE_DIRECTORIES(
./containers)
# 库文件路径，按需
LINK_DIRECTORIES(/usr/lib
/usr/local/lib)
# 主要就是这个，教cmake去找nvcc来编译这些东西
CUDA_ADD_EXECUTABLE(ad-census
main.cu
./containers/device_memory.cpp
./containers/initialization.cpp
)
# 链接外部库，按需
TARGET_LINK_LIBRARIES(ad-census
某个库的名字)需要nvcc指定GPU计算能力？
显存泄露检测
使用cuda自带工具cuda-memcheck
cuda-memcheck --leak-check full ./qrestore_denoise --model ../model_190_g_eval.pt --yuv ../jiaoyu.yuv --width 1920 --height 1080 --device GPU --model_type rgb &gt;&gt; memcheck-mem_guard_7.txt
但是要注意在程序退出前添加cudaDeviceReset(); 需要#include <cuda_runtime.h>
手动释放libtorch占用的显存
#include &quot;torch/utils.h&quot; #include &lt;c10/cuda/CUDACachingAllocator.h&gt; c10::cuda::CUDACachingAllocator::emptyCache();
C10_CUDA_API DeviceStats getDeviceStats(int device);
libtorch 1.3的接口
void display_c10_cuda_mem_stat(int32_t sleep_time) {
printf(&quot;currentMemoryAllocated/[maxMemoryAllocated]: \t %0.1f/[%0.1f] MB\n &quot;,
c10::cuda::CUDACachingAllocator::currentMemoryAllocated(0) / 1024.0 / 1024.0,
c10::cuda::CUDACachingAllocator::maxMemoryAllocated(0) / 1024.0 / 1024.0);
printf(&quot;currentMemoryCached/[maxMemoryCached]: \t %0.1f/[%0.1f] MB\n&quot;,
c10::cuda::CUDACachingAllocator::currentMemoryCached(0) / 1024.0 / 1024.0,
c10::cuda::CUDACachingAllocator::maxMemoryCached(0) / 1024.0 / 1024.0);
std::this_thread::sleep_for(std::chrono::milliseconds(1000*sleep_time));
}ffmpeg编debug版和release版
--disable-optimizations
C中可以通过#include <stdio.h>和#include "stidio.h"，区别是：
#include &lt;stdio.h&gt;，直接到系统指定目录去查找头文件。
include "stidio.h"，会先到当前目录查找头文件，如果没找到在到系统指定目录查找。
gcc编译时查找头文件，按照以下路径顺序查找：
- gcc编译时，可以设置-I选项以指定头文件的搜索路径，如果指定多个路径，则按照顺序依次查找。比如，
gcc -I /usr/local/include/node a.c
2. gcc会查找环境变量C_INCLUDE_PATH，CPLUS_INCLUDE_PATH中指定的路径。
- 系统默认的路径，分别是/usr/include，/usr/local/include，/usr/lib/gcc-lib/i386-linux/2.95.2/include（gcc库文件的路径，各个系统不一致）。
同时，include也可以采用相对路径，比如，a.c需要包含/usr/local/include/node/v8.h，由于/usr/local/include是系统的默认搜索路径，所以在a.c中可以用相对路径包含，#include<node/v8.h>。
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版权声明：本文为CSDN博主「chosen0ne」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/chosen0ne/article/details/7210946
opencv头文件问题
The problem is that under the "include" directory for "opencv2" there seem to be tons of header files missing. The only header file there at the moment is "opencv.hpp", which includes a whole set of other files which aren't there. Does anyone have any idea where I can get these files from?
The header files of the modules are in their own directories. E.g., you can find calib3d.hpp in /modules/calib3d/include/opencv2/calib3d. The Makefile created by CMake knows these addresses, hence when you make install the header files are all copied into /usr/local/include/opencv2.
Ahh I had only issued a make command and needed to do a make install! Thanks! Do you know how I can set the install path to change it from /usr/local/include etc.? Thanks again!
call cmake -DCMAKE_INSTALL_PREFIX=/usr or any other directory you want instead of /usr.
#include &lt;cuda_runtime.h&gt; cudaGetDeviceCount(&amp;num_devices); cudaSetDevice(cuda_device);
module.eval();
-i /data/zhuyinghao/qrs_2.0.1_online_1225/jiaoye/in/clip/new_clip/我就是演员之巅峰对决-20191221_clip_2.mkv -vf qrestore2=model=torch_rgb:model_path=./qrestore_model/model_0422_190_G.pt:device=gpu:denoise=0 -loglevel info -c:v libx264 -x264opts qp=12:min-keyint=25:keyint=128
jarvis create-runonce-job &quot;runonce-name&quot; --volume-name &quot;jiezhen_sx&quot; --volume-token &quot;375add&quot; --cpu-quota 24 --gpu-quota 2 --mems-in-mb 64000 --cluster-name &quot;runonce-online01-gpu-training&quot; --group &quot;yinghao&quot;
int main(int argc, char** argv)
{
try
{
Args args;
if (argc &lt; 2)
{
printHelp();
args.camera_id = 0;
args.src_is_camera = true;
}
else
{
args = Args::read(argc, argv);
if (help_showed)
return -1;
}
App app(args);
app.run();
}
catch (const Exception&amp; e) { return cout &lt;&lt; &quot;error: &quot; &lt;&lt; e.what() &lt;&lt; endl, 1; }
catch (const exception&amp; e) { return cout &lt;&lt; &quot;error: &quot; &lt;&lt; e.what() &lt;&lt; endl, 1; }
catch(...) { return cout &lt;&lt; &quot;unknown exception&quot; &lt;&lt; endl, 1; }
return 0;
}
0.6987
0.6810
0.7456
wopa0.7356
qing0.7772
./ffmpeg -i E-003-jiaoyu_pianduan-5s.mp4 -vf roibila=roi_model_path=./qrestore_model/run-0-final_cpu.pt:device=gpu -y a.mp4
好进化机器
export QB=root,10.57.211.144,22;ssh haojinhua_sx@jumpbox.qiyi.domain -o SendEnv=QB
(initScaleNets_filter): ModuleList(
(0): Conv2d(6, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU()
(2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU()
(4): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
(5): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): ReLU()
(7): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
(8): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU()
(10): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
(11): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(12): ReLU()
(13): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
(14): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU()
(16): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
(17): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): ReLU()
(19): Upsample(scale_factor=2.0, mode=bilinear)
(20): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(21): ReLU()
(22): Upsample(scale_factor=2.0, mode=bilinear)
(23): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(24): ReLU()
(25): Upsample(scale_factor=2.0, mode=bilinear)
(26): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(27): ReLU()
(28): Upsample(scale_factor=2.0, mode=bilinear)
(29): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(30): ReLU()
(31): Upsample(scale_factor=2.0, mode=bilinear)
(32): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(33): ReLU()
)
(initScaleNets_filter1): ModuleList(
(0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU()
(2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(initScaleNets_filter2): ModuleList(
(0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU()
(2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(flownets): PWCDCNet(
(conv1a): Sequential(
(0): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv1aa): Sequential(
(0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv1b): Sequential(
(0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv2a): Sequential(
(0): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv2aa): Sequential(
(0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv2b): Sequential(
(0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv3a): Sequential(
(0): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv3aa): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv3b): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv4a): Sequential(
(0): Conv2d(64, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv4aa): Sequential(
(0): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv4b): Sequential(
(0): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv5a): Sequential(
(0): Conv2d(96, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv5aa): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv5b): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv6aa): Sequential(
(0): Conv2d(128, 196, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv6a): Sequential(
(0): Conv2d(196, 196, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv6b): Sequential(
(0): Conv2d(196, 196, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(corr): Correlation()
(leakyRELU): LeakyReLU(negative_slope=0.1)
(conv6_0): Sequential(
(0): Conv2d(81, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv6_1): Sequential(
(0): Conv2d(209, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv6_2): Sequential(
(0): Conv2d(337, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv6_3): Sequential(
(0): Conv2d(433, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv6_4): Sequential(
(0): Conv2d(497, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(predict_flow6): Conv2d(529, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(deconv6): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(upfeat6): ConvTranspose2d(529, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv5_0): Sequential(
(0): Conv2d(213, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv5_1): Sequential(
(0): Conv2d(341, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv5_2): Sequential(
(0): Conv2d(469, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv5_3): Sequential(
(0): Conv2d(565, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv5_4): Sequential(
(0): Conv2d(629, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(predict_flow5): Conv2d(661, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(deconv5): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(upfeat5): ConvTranspose2d(661, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv4_0): Sequential(
(0): Conv2d(181, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv4_1): Sequential(
(0): Conv2d(309, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv4_2): Sequential(
(0): Conv2d(437, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv4_3): Sequential(
(0): Conv2d(533, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv4_4): Sequential(
(0): Conv2d(597, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(predict_flow4): Conv2d(629, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(deconv4): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(upfeat4): ConvTranspose2d(629, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv3_0): Sequential(
(0): Conv2d(149, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv3_1): Sequential(
(0): Conv2d(277, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv3_2): Sequential(
(0): Conv2d(405, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv3_3): Sequential(
(0): Conv2d(501, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv3_4): Sequential(
(0): Conv2d(565, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(predict_flow3): Conv2d(597, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(deconv3): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(upfeat3): ConvTranspose2d(597, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(conv2_0): Sequential(
(0): Conv2d(117, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv2_1): Sequential(
(0): Conv2d(245, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv2_2): Sequential(
(0): Conv2d(373, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv2_3): Sequential(
(0): Conv2d(469, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(conv2_4): Sequential(
(0): Conv2d(533, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(predict_flow2): Conv2d(565, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(deconv2): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(dc_conv1): Sequential(
(0): Conv2d(565, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(dc_conv2): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2))
(1): LeakyReLU(negative_slope=0.1)
)
(dc_conv3): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4))
(1): LeakyReLU(negative_slope=0.1)
)
(dc_conv4): Sequential(
(0): Conv2d(128, 96, kernel_size=(3, 3), stride=(1, 1), padding=(8, 8), dilation=(8, 8))
(1): LeakyReLU(negative_slope=0.1)
)
(dc_conv5): Sequential(
(0): Conv2d(96, 64, kernel_size=(3, 3), stride=(1, 1), padding=(16, 16), dilation=(16, 16))
(1): LeakyReLU(negative_slope=0.1)
)
(dc_conv6): Sequential(
(0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.1)
)
(dc_conv7): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(depthNet): Sequential(
(0): Conv2d(3, 128, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Sequential(
(0): LambdaMap(
(0): Sequential(
(0): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
(1): LambdaReduce(
(0): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 32, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(2): LambdaReduce(
(0): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 32, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(3): Sequential(
(0): LambdaMap(
(0): Sequential(
(0): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
(1): LambdaReduce(
(0): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 32, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(2): LambdaReduce(
(0): Sequential(
(0): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(3): Sequential(
(0): LambdaMap(
(0): Sequential(
(0): LambdaReduce(
(0): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(1): LambdaReduce(
(0): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 64, kernel_size=(11, 11), stride=(1, 1), padding=(5, 5))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
)
(1): Sequential(
(0): AvgPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0)
(1): LambdaReduce(
(0): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(2): LambdaReduce(
(0): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(3): Sequential(
(0): LambdaMap(
(0): Sequential(
(0): LambdaReduce(
(0): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(1): LambdaReduce(
(0): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
)
(1): Sequential(
(0): AvgPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0)
(1): LambdaReduce(
(0): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(2): LambdaReduce(
(0): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(3): LambdaReduce(
(0): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(4): UpsamplingNearest2d(scale_factor=2.0, mode=nearest)
)
)
(1): LambdaReduce()
)
(4): LambdaReduce(
(0): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(5): LambdaReduce(
(0): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 64, kernel_size=(11, 11), stride=(1, 1), padding=(5, 5))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(6): UpsamplingNearest2d(scale_factor=2.0, mode=nearest)
)
)
(1): LambdaReduce()
)
(4): LambdaReduce(
(0): Sequential(
(0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(5): LambdaReduce(
(0): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 32, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(6): UpsamplingNearest2d(scale_factor=2.0, mode=nearest)
)
(1): Sequential(
(0): LambdaReduce(
(0): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 32, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(1): LambdaReduce(
(0): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 32, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 32, kernel_size=(11, 11), stride=(1, 1), padding=(5, 5))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
)
)
(1): LambdaReduce()
)
(4): LambdaReduce(
(0): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 32, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(5): LambdaReduce(
(0): Sequential(
(0): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 16, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(32, 16, kernel_size=(11, 11), stride=(1, 1), padding=(5, 5))
(4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
(6): UpsamplingNearest2d(scale_factor=2.0, mode=nearest)
)
(1): Sequential(
(0): LambdaReduce(
(0): Sequential(
(0): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(2): Sequential(
(0): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 16, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
(3): Sequential(
(0): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 16, kernel_size=(11, 11), stride=(1, 1), padding=(5, 5))
(4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(5): ReLU()
)
)
)
)
(1): LambdaReduce()
)
(4): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
))pip install scikit-image -i http://jfrog.cloud.qiyi.domain/api/pypi/pypi/simple Looking in indexes: http://jfrog.cloud.qiyi.domain/api/pypi/pypi/simple
for file in `ls /data/zhuyinghao/zoomai/25_to_120/`;do ffmpeg -i /data/zhuyinghao/zoomai/zoomai_120fps/${file%%_*}_120fps.mp4 -i /data/zhuyinghao/zoomai/25_to_120/$file -filter_complex hstack=inputs=2 -vcodec libx264 -x264opts qp=12:bframes=3 -color_primaries 1 -color_trc 1 -colorspace 1 -y ./compare/${file%%.*}_zoom_vs_our_120fps.mp4;doneconvert png转换raw
convert -depth 8 xxx.png rgb:xxx.raw
==132134== Profiling result:
Type Time(%) Time Calls Avg Min Max Name
GPU activities: 47.44% 9.9232ms 1 9.9232ms 9.9232ms 9.9232ms scharr3x3(uchar3*, unsigned char*, short, short, short, short, unsigned int)
33.48% 7.0028ms 2 3.5014ms 3.4969ms 3.5059ms morph(unsigned char*, unsigned char*, int, int, int, int, int, bool)
9.56% 2.0005ms 1 2.0005ms 2.0005ms 2.0005ms block_label(unsigned int*, unsigned char*)
3.66% 766.24us 8 95.779us 768ns 760.41us [CUDA memcpy HtoD]
1.48% 308.75us 1 308.75us 308.75us 308.75us resolve_labels(unsigned int*)
1.27% 266.25us 1 266.25us 266.25us 266.25us y_label_reduction(unsigned int*, unsigned char*)
1.11% 232.33us 1 232.33us 232.33us 232.33us hist(unsigned int*, unsigned int*, int)
0.79% 165.45us 1 165.45us 165.45us 165.45us reducedot(unsigned char*, unsigned int*, unsigned int*, int)
0.77% 161.80us 1 161.80us 161.80us 161.80us [CUDA memcpy DtoH]
0.42% 88.035us 1 88.035us 88.035us 88.035us x_label_reduction(unsigned int*, unsigned char*)
0.00% 736ns 1 736ns 736ns 736ns [CUDA memset]
API calls: 92.35% 287.31ms 4 71.827ms 248.97us 286.55ms cudaMalloc
6.76% 21.047ms 2 10.524ms 882.63us 20.165ms cudaMemcpy
0.39% 1.2074ms 1 1.2074ms 1.2074ms 1.2074ms cuDeviceTotalMem
0.35% 1.0967ms 96 11.424us 165ns 511.02us cuDeviceGetAttribute
0.04% 121.71us 9 13.523us 7.3820us 37.743us cudaLaunchKernel
0.04% 114.73us 7 16.389us 6.9070us 70.432us cudaMemcpyToSymbol
0.03% 104.82us 1 104.82us 104.82us 104.82us cuDeviceGetName
0.02% 71.966us 1 71.966us 71.966us 71.966us cudaMemsetAsync
0.01% 31.316us 3 10.438us 2.6450us 26.019us cudaStreamCreate
0.00% 9.7070us 1 9.7070us 9.7070us 9.7070us cudaStreamSynchronize
0.00% 4.8730us 1 4.8730us 4.8730us 4.8730us cuDeviceGetPCIBusId
0.00% 2.6550us 3 885ns 292ns 1.8670us cuDeviceGetCount
0.00% 1.4150us 2 707ns 249ns 1.1660us cuDeviceGet
0.00% 301ns 1 301ns 301ns 301ns cuDeviceGetUuid```curl -X PUT -T test_dain_small.py -H &quot;X-Auth-Token: dad42a3b66aa40c5962d35540d9ab052&quot; https://fft.qiyi.domain/api/file/4k_3d_60fps/test_dain_small.py
for file in `ls ./`;do echo $file;curl -X PUT -T $file -H &quot;X-Auth-Token: dad42a3b66aa40c5962d35540d9ab052&quot; http://fft.qiyi.domain/api/file/4k_3d_fruc/24fps/$file;done
jarvis create-runonce-job &quot;ubuntu&quot; --volume-name &quot;zhuyinghao-zhuyinghao&quot; --volume-token &quot;392a0dac0ea14a71b073242870438614&quot; --cpu-quota 12 --gpu-quota 1 --mems-in-mb 32000 --cluster-name &quot;runonce-online03-gpu-training&quot; --group &quot;yinghao&quot;
i=0;cat url | while read line;do ffmpeg -i &quot;$line&quot; -ss 00:05:00 -t 120 -acodec copy -vcodec copy -y ./3d/3d_$(printf %04d $i)_clip_2min.mkv;i=$((i+1));done
将文件夹中的图片转换成视频
ffmpeg -loop 1 -f image2 -i ./%04d.png -vcodec libx264 -x264opts qp=12:bframes=3 -r 1 -frames 14 test_sdyjq.mp4
ffmpeg -pix_fmt gbrp16le -s 512x288 -framerate 25.0 -f rawvideo -i /data/dataset/gbr16_video.rgb -c:v libx265 -x265-params colorprim=&#39;bt2020&#39;:transfer=&#39;smpte-st-2084&#39;:colormatrix=&#39;bt2020nc&#39; -preset slow -pix_fmt yuv420p10le /data/dataset/hdr10_test_video.mkv ffmpeg -pix_fmt gbrp16le -s 1920x1080 -framerate 25.0 -f rawvideo -i src-tenggongxueyuan-sdr-4-014633-30s.gbr -c:v libx265 -preset veryslow -x265-params lossless -pix_fmt yuv420p10le src-tenggongxueyuan-sdr-4-014633-30s.mp4 ffmpeg -pix_fmt gbrp16le -s 1920x1080 -framerate 25.0 -f rawvideo -i src-tenggongxueyuan-sdr-1-001542-30s_enhanced_pq2100_bt2020_16bit.gbr -c:v libx265 -x265-params &quot;hrd=1:aud=1:no-info=1:sar=&#39;1:1&#39;:colorprim=&#39;bt2020&#39;:transfer=&#39;smpte2084&#39;:colormatrix=&#39;bt2020nc&#39;:master-display=&#39;G(8500,39850)B(6500,2300)R(35400,14600)WP(15635,16450)L(0,0)&#39;:max-cll=&#39;0,0&#39;:no-open-gop=1&quot; -b:v 10000k -preset slow -pix_fmt yuv420p10le src-tenggongxueyuan-sdr-1-001542-30s_enhanced_pq2100_bt2020_10M_10bit.mp4
ffmpeg -i 流浪地球.mkv -vf crop=512:512:512:512 -threads 5 -preset ultrafast -strict -2 outputname.mp4
CARAFE: Content-Aware ReAssembly of FEatures
ffmpeg -i shuangzishashou_all.mp4 -vf "movie=logo.png[logo];[in][logo]overlay=x='if(gte(t,2),-w+(mod(t,40)-2)*40,NAN)':y=(main_h-overlay_h)/2 [out]" -t 50 output.mp4
ffmpeg -i ./zongyi_zoomai/vlog营业中11200919800_SR_20200423.mkv -i edvr_M_tsa/vlog营业中11200919800_restore_by_223300_G_l1_and_perceptrul_qrestore.mp4 -filter_complex hstack=inputs=2 -t 10 -acodec copy -vcodec libx264 -x264opts qp=12:bframes=3 compare-vlog.mp4
rgb2gray
# rgb_weights = [0.3, 0.59, 0.11]
# roi_crop_gray = np.dot(roi_crop[...,:3], rgb_weights)nvcc -w -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_72,code=sm_72 -gencode=arch=compute_75,code=sm_75 -lib kernel.cu -o libfruckernel.a g++ main.o -o main -L/usr/local/cuda/lib64 -L/data/zhuyinghao/TensorRT-7.0.0.11/lib -lmyelin -lcublas -lnvrtc -lnvinfer -lcudart -lnvparsers -lnvonnxparser -L. -lfruckernel
投递拼多多集团-PDD等公司10个岗位
海康威视工作强度 82人发布